class_docstr: Abstract base class for all attack classes.
class_docstr_tok: ["Abstract","base","class","for","all","attack","classes","."]
docstr: Generate the attack's symbolic graph for adversarial examples. This
  method should be overriden in any child class that implements an
  attack that is expressable symbolically. Otherwise, it will wrap the
  numerical implementation as a symbolic operator.
  
  :param x: The model's symbolic inputs.
  :param **kwargs: optional parameters used by child classes.
    Each child class defines additional parameters as needed.
    Child classes that use the following concepts should use the following
    names:
      clip_min: minimum feature value
      clip_max: maximum feature value
      eps: size of norm constraint on adversarial perturbation
      ord: order of norm constraint
      nb_iter: number of iterations
      eps_iter: size of norm constraint on iteration
      y_target: if specified, the attack is targeted.
      y: Do not specify if y_target is specified.
         If specified, the attack is untargeted, aims to make the output
         class not be y.
         If neither y_target nor y is specified, y is inferred by having
         the model classify the input.
    For other concepts, it's generally a good idea to read other classes
    and check for name consistency.
  :return: A symbolic representation of the adversarial examples.
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code: def generate(self, x, **kwargs):
      """
      Generate the attack's symbolic graph for adversarial examples. This
      method should be overriden in any child class that implements an
      attack that is expressable symbolically. Otherwise, it will wrap the
      numerical implementation as a symbolic operator.
  
      :param x: The model's symbolic inputs.
      :param **kwargs: optional parameters used by child classes.
        Each child class defines additional parameters as needed.
        Child classes that use the following concepts should use the following
        names:
          clip_min: minimum feature value
          clip_max: maximum feature value
          eps: size of norm constraint on adversarial perturbation
          ord: order of norm constraint
          nb_iter: number of iterations
          eps_iter: size of norm constraint on iteration
          y_target: if specified, the attack is targeted.
          y: Do not specify if y_target is specified.
             If specified, the attack is untargeted, aims to make the output
             class not be y.
             If neither y_target nor y is specified, y is inferred by having
             the model classify the input.
        For other concepts, it's generally a good idea to read other classes
        and check for name consistency.
      :return: A symbolic representation of the adversarial examples.
      """
  
      error = "Sub-classes must implement generate."
      raise NotImplementedError(error)
      # Include an unused return so pylint understands the method signature
      return x
  
file_path: cleverhans/cleverhans_v3.1.0/cleverhans/attacks/attack.py
imports: from abc import ABCMeta
  import collections
  import warnings
  import numpy
  import tensorflow
  from cleverhans.compat import reduce_max
  from cleverhans.model import Model
  from cleverhans import utils
label_desc: Use image node_id 2 for calling the Attack obj's underlying member method code with example usage: obj.generate(x) and returns: x